Cross-sectional Studies

In a cross-sectional study, data are collected on the whole study population at a single point in time to examine the relationship between disease (or other health related state) and other variables of interest.

Cross-sectional studies therefore provide a snapshot of the frequency of a disease or other health related characteristics in a population at a given point in time. This methodology can be used to assess the burden of disease or health needs of a population, for example, and is therefore particularly useful in informing the planning and allocation of health resources.

Types of cross-sectional study

Descriptive

A cross-sectional study may be purely descriptive and used to assess the frequency and distribution of a particular disease in a defined population. For example a random sample of schools across London may be used to assess the burden or prevalence of asthma among 12-14 year olds.

Analytical

Analytical cross-sectional studies may also be used to investigate the association between a putative risk factor and a health outcome. However this type of study is limited in its ability to draw valid conclusions about any association or possible causality because the presence of risk factors and outcomes are measured simultaneously. It may therefore be difficult to work out whether the disease or the exposure came first, so causation should always be confirmed by more rigorous studies. The collection of information about risk factors is also retrospective, running the risk of recall bias.

In practice cross-sectional studies will include an element of both types of design.

Issues in the design of cross-sectional surveys

Choosing a representative sample

A cross-sectional study should be representative of whole the population, if generalisations from the findings are to have any validity. For example a study of the prevalence of diabetes among women aged 40-60 years in Town A should comprise a random sample of all women aged 40-60 years in that town. If the study is to be representative, attempts should be made to include hard to reach groups, such as people in institutions or the homeless.

Sample size

The sample size should be sufficiently large enough to estimate the prevalence of the conditions of interest with adequate precision. Sample size calculations can be carried out using sample size tables or statistical packages such as Epi Info. The larger the study, the less likely the results are due to chance alone, but this will also have implications for cost.

Data collection

As data on exposures and outcomes are collected simultaneously, specific inclusion and exclusion criteria should be established at the design stage, to ensure that those with the outcome are correctly identified. The data collection methods will depend on the exposure, outcome and study setting, but include questionnaires and interviews, as well as medical examinations. Routine data sources may also be used.

Potential bias in cross-sectional studies

Non-response is a particular problem affecting cross-sectional studies and can result in bias of the measures of outcome. This is a particular problem when the characteristics of non-responders differ from responders.

Analysis of cross-sectional studies

In a cross-sectional study all factors (exposure, outcome, and confounders) are measured simultaneously. The main outcome measure obtained from a cross-sectional study is prevalence:

For continuous variables such as blood pressure or weight, values will fall along a continuum within a given range. Prevalence may therefore only be calculated when the variable is divided into those values that fall below or above a particular pre-determined level. Alternatively mean or median levels may be calculated.

In analytical cross-sectional studies the odds ratio can be used to assess the strength of an association between a risk factor and health outcome of interest, provided that the current exposure accurately reflects the past exposure.

Strengths and weaknesses of cross-sectional studies

Strengths

Relatively quick and easy to conduct (no long periods of follow-up).

Data on all variables is only collected once.

Able to measure prevalence for all factors under investigation.

Multiple outcomes and exposures can be studied.

The prevalence of disease or other health related characteristics are important in public health for assessing the burden of disease in a specified population and in planning and allocating health resources.

Good for descriptive analyses and for generating hypotheses.

Weaknesses

Difficult to determine whether the outcome followed exposure in time or exposure resulted from the outcome.

Not suitable for studying rare diseases or diseases with a short duration.

As cross-sectional studies measure prevalent rather than incident cases, the data will always reflect determinants of survival as well as aetiology.1

Unable to measure incidence.

Associations identified may be difficult to interpret.

Susceptible to bias due to low response and misclassification due to recall bias.